add warnings (#5516)

configs/datasets/coco_detection.yml

@@ -17,3 +17,4 @@ EvalDataset:
 TestDataset:
   !ImageFolder
     anno_path: annotations/instances_val2017.json
+    dataset_dir: dataset/coco

configs/datasets/coco_instance.yml

@@ -17,3 +17,4 @@ EvalDataset:
 TestDataset:
   !ImageFolder
     anno_path: annotations/instances_val2017.json
+    dataset_dir: dataset/coco

configs/datasets/dota.yml

@@ -17,3 +17,4 @@ EvalDataset:
 TestDataset:
   !ImageFolder
     anno_path: trainval_split/s2anet_trainval_paddle_coco.json
+    dataset_dir: dataset/DOTA_1024_s2anet/

configs/mot/bytetrack/_base_/mot17.yml

@@ -17,6 +17,7 @@ EvalDataset:
 
 TestDataset:
   !ImageFolder
+    dataset_dir: dataset/mot/MOT17
     anno_path: annotations/val_half.json
 
 

configs/mot/deepsort/_base_/mot17.yml

@@ -17,6 +17,7 @@ EvalDataset:
 
 TestDataset:
   !ImageFolder
+    dataset_dir: dataset/mot/MOT17
     anno_path: annotations/val_half.json
 
 

ppdet/data/source/category.py

@@ -39,6 +39,11 @@ def get_categories(metric_type, anno_file=None, arch=None):
     if arch == 'keypoint_arch':
         return (None, {'id': 'keypoint'})
 
+    if anno_file == None or (not os.path.isfile(anno_file)):
+        logger.warning("anno_file '{}' is None or not set or not exist, "
+            "please recheck TrainDataset/EvalDataset/TestDataset.anno_path, "
+            "otherwise the default categories will be used by metric_type.".format(anno_file))
+
     if metric_type.lower() == 'coco' or metric_type.lower(
     ) == 'rbox' or metric_type.lower() == 'snipercoco':
         if anno_file and os.path.isfile(anno_file):
@@ -55,8 +60,9 @@ def get_categories(metric_type, anno_file=None, arch=None):
         # anno file not exist, load default categories of COCO17
         else:
             if metric_type.lower() == 'rbox':
+                logger.warning("metric_type: {}, load default categories of DOTA.".format(metric_type))
                 return _dota_category()
-
+            logger.warning("metric_type: {}, load default categories of COCO.".format(metric_type))
             return _coco17_category()
 
     elif metric_type.lower() == 'voc':
@@ -77,6 +83,7 @@ def get_categories(metric_type, anno_file=None, arch=None):
         # anno file not exist, load default categories of
         # VOC all 20 categories
         else:
+            logger.warning("metric_type: {}, load default categories of VOC.".format(metric_type))
             return _vocall_category()
 
     elif metric_type.lower() == 'oid':
@@ -104,6 +111,7 @@ def get_categories(metric_type, anno_file=None, arch=None):
             return clsid2catid, catid2name
         # anno file not exist, load default category 'pedestrian'.
         else:
+            logger.warning("metric_type: {}, load default categories of pedestrian MOT.".format(metric_type))
             return _mot_category(category='pedestrian')
 
     elif metric_type.lower() in ['kitti', 'bdd100kmot']:
@@ -122,6 +130,7 @@ def get_categories(metric_type, anno_file=None, arch=None):
             return clsid2catid, catid2name
         # anno file not exist, load default categories of visdrone all 10 categories
         else:
+            logger.warning("metric_type: {}, load default categories of VisDrone.".format(metric_type))
             return _visdrone_category()
 
     else:

ppdet/metrics/mcmot_metrics.py

@@ -26,8 +26,6 @@ from motmetrics.math_util import quiet_divide
 import numpy as np
 import pandas as pd
 
-import paddle
-import paddle.nn.functional as F
 from .metrics import Metric
 import motmetrics as mm
 import openpyxl
@@ -311,6 +309,8 @@ class MCMOTEvaluator(object):
         self.gt_filename = os.path.join(self.data_root, '../',
                                         'sequences',
                                         '{}.txt'.format(self.seq_name))
+        if not os.path.exists(self.gt_filename):
+            logger.warning("gt_filename '{}' of MCMOTEvaluator is not exist, so the MOTA will be -inf.")
 
     def reset_accumulator(self):
         import motmetrics as mm

ppdet/metrics/mot_metrics.py

@@ -22,8 +22,7 @@ import sys
 import math
 from collections import defaultdict
 import numpy as np
-import paddle
-import paddle.nn.functional as F
+
 from ppdet.modeling.bbox_utils import bbox_iou_np_expand
 from .map_utils import ap_per_class
 from .metrics import Metric
@@ -36,8 +35,10 @@ __all__ = ['MOTEvaluator', 'MOTMetric', 'JDEDetMetric', 'KITTIMOTMetric']
 
 
 def read_mot_results(filename, is_gt=False, is_ignore=False):
-    valid_labels = {1}
-    ignore_labels = {2, 7, 8, 12}  # only in motchallenge datasets like 'MOT16'
+    valid_label = [1]
+    ignore_labels = [2, 7, 8, 12]  # only in motchallenge datasets like 'MOT16'
+    logger.info("In MOT16/17 dataset the valid_label of ground truth is '{}', "
+        "in other dataset it should be '0' for single classs MOT.".format(valid_label[0]))
     results_dict = dict()
     if os.path.isfile(filename):
         with open(filename, 'r') as f:
@@ -50,12 +51,10 @@ def read_mot_results(filename, is_gt=False, is_ignore=False):
                     continue
                 results_dict.setdefault(fid, list())
 
-                box_size = float(linelist[4]) * float(linelist[5])
-
                 if is_gt:
                     label = int(float(linelist[7]))
                     mark = int(float(linelist[6]))
-                    if mark == 0 or label not in valid_labels:
+                    if mark == 0 or label not in valid_label:
                         continue
                     score = 1
                 elif is_ignore:
@@ -118,6 +117,8 @@ class MOTEvaluator(object):
         assert self.data_type == 'mot'
         gt_filename = os.path.join(self.data_root, self.seq_name, 'gt',
                                    'gt.txt')
+        if not os.path.exists(gt_filename):
+            logger.warning("gt_filename '{}' of MOTEvaluator is not exist, so the MOTA will be -inf.")
         self.gt_frame_dict = read_mot_results(gt_filename, is_gt=True)
         self.gt_ignore_frame_dict = read_mot_results(
             gt_filename, is_ignore=True)